Apparatus and method for processing images

ABSTRACT

Provided are an image processing apparatus and method for restoring a high resolution of an image based on acquired image data using color information. The image processing apparatus selects at least one pixel from among pixels corresponding to narrow-band color information of image data, and estimates wide-band color information of the selected pixel.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit under 35 U.S.C. §119(a) of a KoreanPatent Application No. 10-2010-0070568, filed on Jul. 21, 2010, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND

1. Field

The following description relates to an image processing apparatus andmethod for restoring a high resolution of an image using colorinformation that is included in acquired image data.

2. Description of the Related Art

Existing image processing apparatuses have difficulty in acquiringhigh-quality images in a various environments such as a low-lightenvironment. In order to acquire high-quality images in a particularenvironment such as an environment that has low-light conditions, animage sensor capable of absorbing a wide wavelength range of light isused. Absorbing a wide wavelength range of light to create imagestypically results in an image with a low resolution.

An existing Bayer pattern (2×2) may maintain at least ½ resolutionbecause it has two Green channels. However, because new patternsincluding only one Green channel are being developed, it is inevitablethat a resolution will be reduced to ¼ or less.

For these reasons, studies into a technology that can restorehigh-quality, high-resolution images while absorbing a wide wavelengthrange of light are being researched.

SUMMARY

In one general aspect, there is provided an image processing apparatusincluding a selector configured to select at least one pixel from amongpixels that correspond to narrow-band color information of image datathat includes narrow-band color information and wide-band colorinformation, and an estimator configured to estimate wide-band colorinformation of the selected pixel, based on narrow-band colorinformation of the selected pixel, narrow-band color information ofpixels that are adjacent to the selected pixel, and correlation valuesbetween the narrow-band color information and the wide-band colorinformation.

The image processing apparatus may further comprise a region classifyingunit configured to classify the image data into a homogeneity region anda non-homogeneity region using a Homogeneity theorem.

The selector may be configured to select at least one pixel from amongpixels that correspond to the narrow-band color information in thenon-homogeneity region.

The estimator may be further configured to estimate wide-band colorinformation of a pixel that is not selected by the selector, based onthe wide-band color information of the image data and the wide-bandcolor information estimated by the estimator.

The estimator may be configured to estimate wide-band color informationof pixels that correspond to the narrow-band color information in thehomogeneity region, based on the wide-band color information of theimage data in the homogeneity region.

The narrow-band color information of pixels that are adjacent to theselected pixel may be narrow-band color information of four pixels, andthe four pixels may include the pixels that are directly above, below,to the left, and to the right of the at least one selected pixel.

The image processing apparatus may further comprise a calculatorconfigured to divide the image data into 2×2 blocks that each includethree pixels corresponding to the narrow-band color information and onepixel corresponding to the wide-band color information, and to calculatecorrelation values between the wide-band color information and thenarrow-band color information included in each 2×2 block.

The selector may be configured to select at least one pixel from amongpixels that correspond to the narrow-band color information, which arelocated diagonal to pixels corresponding to wide-band color informationof the image data.

The image processing apparatus may further comprise an image acquiringunit configured to acquire image data that includes narrow-band colorinformation and wide-band color information that correspond to lightstransmitted through a narrow-band color filter and a wide-band colorfilter, respectively, using photo sensors provided for individualpixels.

The narrow-band color information may be color information about lighttransmitted through at least one of a Red (R) color filter, a Green (G)color filter, a Blue (B) color filter, a Cyan (C) color filter, a Yellow(Y) color filter, a Magenta (M) color filter and a Black (K) colorfilter, and the wide-band color information is color information aboutlight transmitted through at least one of a panchromatic filter and aWhite & Near Infrared (WNIR) filter.

In another aspect, there is provided an image processing methodincluding selecting at least one pixel from among pixels that correspondto narrow-band color information of image data that includes thenarrow-band color information and wide-band color information, andestimating wide-band color information of the selected pixel based onnarrow-band color information of the selected pixel, narrow-band colorinformation of pixels that are adjacent to the selected pixels, andcorrelation values between the narrow-band color information and thewide-band color information.

The image processing method may further comprise classifying the imagedata into a homogeneity region and a non-homogeneity region using aHomogeneity theorem.

The selecting of the at least one pixel may comprise selecting at leastone pixel from among pixels that correspond to the narrow-band colorinformation in the non-homogeneity region.

The estimating of the wide-band color information may further compriseestimating wide-band color information of a pixel that is not selected,based on the wide-band color information of the image data and theestimated wide-band color information.

The estimating of the wide-band color information may compriseestimating wide-band color information of pixels that correspond tonarrow-band color information in the homogeneity region, based on thewide-band color information of the image data in the homogeneity region.

The narrow-band color information of the pixels adjacent to the selectedpixel may be narrow-band color information of four pixels, and the fourpixels may include the pixels that are directly above, below, to theleft, and to the right of the at least one selected pixel.

The image processing method may further comprise dividing the image datainto 2×2 blocks that each include three pixels corresponding to thenarrow-band color information and one pixel corresponding to thewide-band color information, and calculating correlation values betweenthe wide-band color information and the narrow-band color informationincluded in each 2×2 block.

The selecting of the at least one pixel may comprise selecting at leastone pixel from among pixels that correspond to the narrow-band colorinformation, which are located diagonal to pixels corresponding to thewide-band color information of the image data.

The image processing method may further comprise acquiring image datathat includes narrow-band color information and wide-band colorinformation that corresponds to lights that are transmitted through anarrow-band color filter and a wide-band color filter, using photosensors provided for individual pixels.

The narrow-band color information may be color information about lighttransmitted through at least one of a Red (R) color filter, a Green (G)color filter, a Blue (B) color filter, a Cyan (C) color filter, a Yellow(Y) color filter, a Magenta (M) color filter and a Black (K) colorfilter, and the wide-band color information is color information aboutlight transmitted through at least one of a panchromatic filter and aWhite & Near Infrared (WNIR) filter.

Other features and aspects may be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of an image processingapparatus.

FIG. 2 is a graph illustrating an example of color spectrums that appearaccording to wavelengths.

FIG. 3 is a flowchart illustrating an example of an image processingmethod.

FIGS. 4A through 4I are diagrams illustrating examples of imageprocessing.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals should be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following description is provided to assist the reader in gaining acomprehensive understanding of the methods, apparatuses, and/or systemsdescribed herein. Accordingly, various changes, modifications, andequivalents of the methods, apparatuses, and/or systems described hereinmay be suggested to those of ordinary skill in the art. Also,descriptions of well-known functions and constructions may be omittedfor increased clarity and conciseness.

FIG. 1 illustrates an example of an image processing apparatus.

Referring to FIG. 1, the image processing apparatus 100 includes animage acquiring unit 110, a region classifying unit 115, a selector 120,a calculator 125, an estimator 130, and a color image creator 140. Theimage processing apparatus 100 may be or may be included in a terminal,for example, a computer, a mobile terminal, a smart phone, a laptopcomputer, a personal digital assistant, a tablet, an MP3 player, and thelike.

The image acquiring unit 110 may acquire image data. The image acquiringunit 110 may include various imaging equipment, for example, a lens, acolor filter, an image sensor, and the like. The image sensor mayrecognize light that passes through the lens and color filter and mayinclude a plurality of photo sensors. For example, there may be a photosensor provided for each pixel. The image sensor may create image databased on the recognized light.

The color filter may include one or more of a Red (R) color filter, aGreen (G) color filter, a Blue (B) color filter, a Cyan (C) colorfilter, a Yellow (Y) color filter, a Magenta (M) color filter, a Black(K) color filter, a panchromatic filter, a White & Near Infrared (WNIR)filter, and the like. For example, the R color filter may allow light topass that has a wavelength corresponding to a Red color. The imagesensor may recognize the light that passes through the color filters tocreate color information. The color information may contain contrastinformation of the corresponding color.

For example, when certain color information is 8 bits, the colorinformation may be represented by a number between 0 and 255. In thisexample, the darkest color may be represented by 0 and the brightestcolor may be represented by 255. The color information is not limited tothis example and may be represented by any desired color information,for example, the color information may be represented by various sizesof bits, such as 12 bits, 16 bits, and the like. The color filter mayinclude a narrow-band filter and a wide-band filter, such as RGBW, CYYW,CMYWNIR, and the like.

The image acquiring unit 110 may acquire narrow-band color informationbased on light that passes through one or more of a R color filter, Gcolor filter, B color filter, C color filter, Y color filter, M colorfilter, K color filter, and the like. For example, the narrow-band colorinformation may include color information about light that has anarrow-band wavelength. The narrow-band wavelength may be a wavelengthregion corresponding to specific colors.

The image acquiring unit 110 may acquire wide-band color informationbased on light that passes through the panchromatic filter and the WNIRfilter. The panchromatic filter may also be referred to as a whitefilter. For example, the wide-band color information may include colorinformation about light that has a wide-band wavelength. The wide-bandwavelength may include a wavelength region corresponding to severalcolors and a Near Infrared wavelength region. In various aspects, theimage acquiring unit 110 may acquire image data including narrow-bandcolor information and wide-band color information, based on light thatpasses through the color filter.

For example, the image acquiring unit 110 may use photo sensors that areprovided for individual pixels to create image data includingnarrow-color information and wide-band color information correspondingto light that is transmitted through the narrow-band color filters andwide-band color filters. Wavelength distributions with respect to colorsare further described with reference to FIG. 2.

The region classifying unit 115 may classify the acquired image data,for example, into a homogeneity region and a non-homogeneity regionusing a Homogeneity theorem. The homogeneity region includes a region ofthe image data where changes in image data between pixels are small. Thenon-homogeneity region includes a region of the image data where changesin image data between pixels are great.

For example, the region classifying unit 115 may calculate horizontalgradient values and vertical gradient values using color informationthat is included in the image data. Then, the region classifying unit115 may calculate a standard deviation based on the horizontal andvertical gradient values. The region classifying unit 115 may classify aregion that has a standard deviation that is smaller than a referencevalue into a homogeneity region and may classify a region that has astandard deviation that is greater than the reference value into anon-homogeneity region. In this example, the reference value is asetting value for distinguishing homogeneity regions fromnon-homogeneity regions.

As another example, the region classifying unit 115 may distinguishhomogeneity regions from non-homogeneity regions using a high passfilter and a convolution computation. It should also be appreciated thatthe region classifying unit 115 may use any other methods to distinguishhomogeneity regions from non-homogeneity regions.

As described herein, the image data includes narrow-band colorinformation and wide-band color information. The selector 120 may selectat least one pixel including narrow-band color information from thenon-homogeneity region of the image data that is acquired by the imageacquiring unit 110. For example, the selector 120 may select at leastone pixel corresponding to narrow-band color information which islocated diagonal to pixels corresponding to wide-band color informationof the image data.

The calculator 125 may divide the non-homogeneity region of the imagedata into blocks, for example, 2×2 blocks that each include three pixelscorresponding to narrow-band color information and one pixelcorresponding to wide-band color information. The calculator 125 maycalculate correlation values between the three pieces of narrow-bandcolor information and one piece of wide-band color information includedin each block.

The estimator 130 may estimate wide-band color information of theselected pixel, based on the narrow-band color information of theselected pixel, narrow-band color information of pixels adjacent to theselected pixel, and/or the correlation values between the acquirednarrow-band color information and the acquired wide-band colorinformation. For example, the narrow-band color information of pixelsthat are adjacent to the selected pixel may be narrow-band colorinformation of four pixels that are positioned above, below, left, andright of the selected pixel.

As another example, the estimator 130 may estimate wide-band colorinformation of a pixel corresponding to narrow-band color information inthe homogeneity region of the image data, based on the acquiredwide-band color information.

The estimator 130 may restore blurred images using an image restorationmethod. For example, the image restoration method may include a Wienefilter algorithm, a Lucy-Richardson algorithm, a Priori Sparsityalgorithm, and the like.

The estimator 130 may estimate wide-band color information of pixelsthat are not selected by the selector 120 in the non-homogeneity region,based on the wide-band color information that is acquired by the imageacquiring unit 110 and the wide-band color information that is estimatedby the estimator 130. For example, the estimator 130 may estimatewide-band color information of pixels that are not selected by theselector 120 using an interpolation method, such as Constant Hue-Basedinterpolation, Edge-Directed interpolation, Median-Based interpolationby freeman. Homogeneity-Directed interpolation by K. Hirakawa and T. W.Parks, and the like. Accordingly, the image processing apparatus 100 mayacquire wide-band color information of all pixels corresponding to theimage data.

The color image creator 140 may create color image data based on imagedata including the estimated wide-band color information and theacquired wide-band color information. Image processing and the resultsof the image processing by individual components illustrated in FIG. 1are further described with reference to FIGS. 4A through 4I.

The current example corresponds to the case in which the imageprocessing apparatus 100 includes the region classifying unit 115,however, the image processing apparatus 100 may not include a regionclassifying unit. For example, the selector 120 may select at least onepixel including narrow-band color information from among the image dataacquired by the image acquiring unit 110. The selector 120 may select atleast one pixel corresponding to narrow-band color information, which islocated diagonal to pixels corresponding to wide-band color informationof the image data.

The calculator 125 may divide the image data into 2×2 blocks that eachinclude three pixels corresponding to narrow-band color information andone pixel corresponding to wide-band color information. The calculator125 may calculate correlation values between the three pieces ofnarrow-band color information and the one piece of wide-band colorinformation included in each block.

Then, the estimator 130 may estimate the wide-band color information ofthe selected pixel, based on the narrow-band color information of theselected pixel, the narrow-band color information of pixels adjacent tothe selected pixel, and/or the correlation values between the acquirednarrow-band color information and the acquired wide-band colorinformation. The estimator 130 may also estimate wide-band colorinformation of pixels that are not selected by the selector 120 based onthe acquired wide-band color information and the estimated wide-bandcolor information. Accordingly, the image processing apparatus 100 mayacquire wide-band color information of all pixels corresponding to theimage data. That is, the image processing apparatus 100 may estimatewide-band color information without dividing image data into homogeneityand non-homogeneity regions.

As described in the example of FIG. 1, the image processing apparatus100 may estimate wide-band color information based only on acquirednarrow-band color information. The image processing apparatus 100 mayestimate wide-band color information of entire image data using a smallamount of acquired wide-band color information. In addition, the imageprocessing apparatus 100 may restore full resolution of an image or nearfull resolution of the image by restoring image data using estimatedwide-band color information.

FIG. 2 is a graph that illustrates examples of color spectrums thatappear according to wavelengths.

In the current example, the unit of wavelength is expressed innanometers (nm) and the unit of sensitivity depends on a predeterminedcriteria. The vertical axis of the graph may be expressed as relativemagnitudes.

Referring to FIG. 2, the B color 200 shows the greatest sensitivityvalue at a wavelength of 450 nm and has a wavelength range ofapproximately 400 nm to 500 nm. The G color 210 shows the greatestsensitivity value at a wavelength of 550 nm and has a wavelength rangeof approximately 500 nm to 600 nm. The R color 220 shows the greatestsensitivity value at a wavelength of 650 nm and has a wavelength rangeof approximately 600 nm to 700 nm. The W color 230 has a wavelengthrange including all of the R, G and B colors 200, 210 and 220. The WNIRextends over all of the W color and NIR regions.

In the example of FIG. 2, the B, G and R colors 200, 210, and 220 areexamples of narrow-band colors and the W color and WNIR are examples ofwide-band colors.

FIG. 3 illustrates an example of an image processing method.

Referring to FIGS. 1 and 3, the image acquiring unit 110 acquires imagedata including narrow-band color information and wide-band colorinformation (300). Whether the image data is classified intonon-homogeneity regions (310). As another example, a region classifyingunit 115 may classify the image data into a homogeneity region and anon-homogeneity region using a Homogeneity theorem (310). In response tothe image data being classified into a non-homogeneity region, theselector 120 selects at least one pixel including narrow-band colorinformation from the non-homogeneity region (320). In 325, thecalculator 125 divides the non-homogeneity region into 2×2 blocks thateach include three pixels that have narrow-band color information andone pixel that has wide-band color information, and calculatescorrelation values between the three pieces of narrow-band colorinformation and the one piece of wide-band color information included ineach block.

In (330) the estimator 130 estimates wide-band color information of theselected pixel based on the narrow-band color information of theselected pixel, narrow-band color information of pixels that areadjacent to the selected pixel, and the correlation values between theacquired narrow-band color information and the acquired wide-band colorinformation. The estimator 130 estimates wide-band color information ofpixels that are not selected by the selector 120 in the non-homogeneityregion, based on the wide-band color information that is acquired by theimage acquiring unit 110 and the wide-band color information that isestimated by the estimator 130 (340). Accordingly, it is possible toestimate wide-band color information of all pixels in thenon-homogeneity region.

In response to the image data being classified into a homogeneityregion, the estimator 130 estimates wide-band color information ofpixels that have narrow-band color information in the homogeneity regionbased on the acquired wide-band color information (350). Accordingly, itis also possible to estimate wide-band color information of all pixelsin the homogeneity region.

The color image creator 150 may create color image data based on theimage data including the estimated wide-band color information and theacquired wide-band color information (360).

As described in the example of FIG. 3, it is possible to estimatewide-band color information based on acquired narrow-band colorinformation. In addition, the image processing method may restore a fullresolution of an image or near full resolution of the image by restoringcolor image data using estimated wide-band color information.

FIGS. 4A through 4I illustrate examples of image processing.

Referring to FIGS. 1 and 4A, the image acquiring unit 110 acquires imagedata that includes narrow-band color information and wide-band colorinformation. Referring to FIG. 4B, the region classifying unit 115classifies the image data into homogeneity regions 410 and 412 and anon-homogeneity region 411 using a Homogeneity theorem.

FIG. 4C is an example in which the homogeneity regions 410 and 412 andthe non-homogeneity region 411 illustrated in FIG. 4B are represented ascolor information in units of pixels. Referring to FIG. 4C, in thehomogeneity regions 410 and 412 and the non-homogeneity region 411, 2×2blocks that each include R, G, B, and W are arranged successively. Itshould be appreciated that a total number of pixels is not limited tothe example illustrated in FIG. 4C. The internal arrangement of each 2×2block may be based on the configuration of a color filter that isincluded in the image acquiring unit 110. For example, if the colorfilter includes R, G, B, and W color filters, each 2×2 block may have anarrangement as illustrated in FIG. 4C. As another example, if the colorfilter includes C, M, Y, and W color filters, each 2×2 block may have anarrangement including C, M, Y, and W. In the example of FIG. 4C, W₁, W₂,W₃, and W₄ correspond to acquired wide-band color information.

Referring to FIG. 4D illustrating the non-homogeneity region 411, theselector 120 may select at least one pixel that includes narrow-bandcolor information from the non-homogeneity region 411. For example, theselector 120 may select pixels corresponding to G₁, G₂, G₃, and G₄. Thecalculator 125 may divide the non-homogeneity region 411 into 2×2 blocksthat each include three pixels corresponding to narrow-band colorinformation and one pixel corresponding to wide-band color information,and may calculate correlation values between the three pieces ofnarrow-band color information and the one piece of wide-band colorinformation included in each block.

For example, the calculator 125 may calculate correlation values C_(r),C_(g), and C_(b) based on the color information that is included in eachblock and Equation 1 below.

$\begin{matrix}{\left\lbrack {W_{1},W_{2},\ldots \mspace{11mu},W_{n}} \right\rbrack = {\left\lbrack {C_{r},C_{g},C_{b}} \right\rbrack \times \begin{bmatrix}R_{1} & G_{1} & B_{1} \\R_{2} & G_{2} & B_{2} \\\; & \vdots & \; \\R_{n} & G_{n} & B_{n}\end{bmatrix}}} & (1)\end{matrix}$

In Equation 1, W represents wide-band color information, R, G, and Brepresent narrow-band color information, and C represents correlationvalues. For example, if the color information is 8 bits, the wide-bandcolor information and narrow-band color information may be representedby numbers between 0 and 255. Equation 1 is merely for purposes ofexample, and the correlation values C_(r), C_(g), and C_(b) may becalculated using any other equations. For example, the correlationvalues may be calculated by assigning weights to the narrow-band colorinformation.

The estimator 130 may estimate the wide-band color information of apixel that is selected by the selector 120 in the non-homogeneityregion. For example, the estimator 130 may estimate the wide-band colorbased on narrow-band color information of the selected pixel,narrow-band color information of pixels that are adjacent to theselected pixel, and/or correlation values between the acquirednarrow-band color information and the acquired wide-band colorinformation. For example, in the case in which a pixel G₄ (422) isselected, the estimator 130 may estimate wide-band color information W₂₄(427) of the selected pixel G₄ using Equation 2 that is described below.The narrow-band color information of R₃, R₄, B₂, and B₄ (421, 423, 424,425) correspond to pixels that are adjacent to the selected pixel G₄.That is, the narrow-band color information of R₃, R₄, B₂, and B₄corresponds to the color information of 4 pixels that are located to theleft, right, top, and bottom of the selected pixel G₄.

$\begin{matrix}{W_{24} = \frac{{C_{r}G_{4}} + {C_{g}\frac{R_{3} + R_{4}}{2}} + {C_{b}\frac{B_{2} + B_{4}}{2}}}{3}} & (2)\end{matrix}$

In Equation 2, G₄ is narrow-band color information of the selectedpixel, R₃, R₄, B₂ and B₄ are narrow-band color information of the pixelsthat are adjacent to the selected pixel, and C_(r), C_(g), and C_(b) arecorrelation values between the acquired narrow-band color informationand the acquired wide-band color information.

For example, when G₄, R₃, R₄, B₂ and B₄ are all 10 and C_(r), C_(g), andC_(b) are all 1, the broadband color information W₂₄ is calculated as10.

The estimator 130 may estimate wide-band color information W₂₁, W₂₂, andW₂₃ of the other selected pixels G₁, G₂ and G₃ using the above-describedprocess. However, Equation 2 is merely for purposes of example, and thewide-band color information may be estimated using any other equations.For example, the wide-band color information may be estimated byassigning a weight to each narrow-band color information obtained byEquation 2.

The estimator 130 may also estimate wide-band color information W₂₅ of apixel that is not selected by the selector 120, based on the wide-bandcolor information W₁ that is acquired by the image acquiring unit 110and the wide-band color information W₂₁ estimated by the estimator 130.The estimator 130 may estimate wide-band color information W₂₆ of apixel that is not selected by the selector 120, based on the wide-bandcolor information W₂ that is acquired by the image acquiring unit 110and the wide-band color information W₂₂ estimated by the estimator 130.

The estimator 130 may estimate wide-band color information W₂₇ of apixel that is not selected by the selector 120, based on the wide-bandcolor information W₃ that is acquired by the image acquiring unit 110and the wide-band color information W₂₃ estimated by the estimator 130.For example, the estimator 130 may use various methods to estimate thewide-band color information of pixels that are not selected, such asConstant Hue-Based interpolation, Edge-Detected interpolation,Median-Based interpolation, Homogeneity base interpolation, and thelike. Similarly, the estimator 130 may estimate wide-band colorinformation W₂₈ (428) of a pixel that is not selected by the selector120, based on the wide-band color information W₄ (426) that is acquiredby the image acquiring unit 110 and the wide-band color information W₂₄(427) estimated by the estimator 130. Accordingly, wide-band colorinformation of all pixels in the non-homogeneity region 411 may beestimated.

FIG. 4E illustrates the homogeneity regions 410. In this example, theestimator 130 may estimate wide-band color information W₃₀, W₃₁, W₃₂,and W₃₃ of pixels corresponding to narrow-band color information in thehomogeneity region 410, based on the acquired wide-band colorinformation W₅, W₆, W₇, and W₈. Accordingly, wide-band color informationfor all of the pixels in the homogeneity regions 410 an 412 may beestimated. For example, the estimator 130 may estimate the wide-bandcolor information W₃₀, W₃₁, W₃₂, and W₃₃ to be the same values as theacquired wide-band color information W₅, W₆, W₇, and W₈. As anotherexample, the estimator 130 may estimate the wide-band color informationW₃₀, W₃₁, W₃₂, and W₃₃ by assigning weights to the acquired wide-bandcolor information W₅, W₆, W₇, and W₈.

Referring to FIGS. 4C and 4F, the estimator 130 may estimate wide-bandcolor information W₃₅, W₃₆, W₃₇, and W₃₈ of pixels corresponding tonarrow-band color information in the other homogeneity region 412, basedon the acquired wide-band color information W₉, W₁₀, W₁₁, and W₁₂.

FIG. 4F is a view that illustrates acquired color information andwide-band color information that is estimated for the non-homogeneityregion 411 and the homogeneity regions 410 and 412. Referring to FIG.4F, the estimator 130 may estimate wide-band color information for thenon-homogeneity region 411 and homogeneity regions 410 and 412.Accordingly, the image processing apparatus 100 may obtain wide-bandcolor information that is estimated by the estimator 130 and wide-bandcolor information W₁, W₂, W₃, W₄, W₅, W₆, W₇, W₈, W₉, W₁₀, W₁₁, and W₁₂which is acquired by the image acquiring unit 110.

Referring to FIG. 4G, the color image creator 140 may estimate R colorinformation based on the wide-band color information illustrated in FIG.4F. For example, the color image creator 140 may estimate narrow-bandcolor information 441, 442, and 443 based on acquired narrow-band colorinformation 440 and the estimated wide-band color information W₃₀. Forexample, narrow-band color information corresponding to wide-band colorinformation 20 may be 10 and narrow-band color information correspondingto wide-band color information 40 may be 20. For example, if theestimated wide-band color information W₃₀ is 40, the color image creator140 may estimate the narrow-band color information 441, 442, and 443 tobe 20.

In this example, the relative magnitudes of the narrow-band colorinformation and the estimated wide-band color information may be set tovarious values by a user. The color image creator 140 may estimatenarrow-band color information based on the relative magnitudes of thenarrow-band color information and the estimated wide-band colorinformation. By the process described herein, the color image creator140 may estimate the remaining narrow-band color information.

Referring to FIG. 4H, the color image creator 140 may estimate G colorinformation based on the wide-band color information that is illustratedin FIG. 4F. For example, the color image creator 140 may estimatenarrow-band color information 451, 452, and 453 based on acquirednarrow-band color information 450 and the estimated wide-band colorinformation W₃₀. For example, narrow-band color informationcorresponding to wide-band color information 20 may be 5 and narrow-bandcolor information corresponding to wide-band color information 40 may be10. For example, if the estimated wide-band color information W₃₀ is 40,the color image creator 140 may estimate narrow-band color information451, 452, and 453 to be 10.

In this example, the relative magnitudes of the narrow-band colorinformation and the wide-band color information may be set to variousvalues by a user. The color image creator 140 may estimate narrow-bandcolor information based on the relative magnitudes of the narrow-bandcolor information and the estimated wide-band color information. By theprocess described herein, the color image creator 140 may estimate theremaining narrow-band color information.

Referring to FIG. 4I, the color image creator 140 may estimate B colorinformation based on the wide-band color information illustrated in FIG.4F. For example, the color image creator 140 may estimate narrow-bandcolor information 461, 462, and 463 based on the acquired narrow-bandcolor information 460 and the estimated wide-band color information W₃₀.For example, narrow-band color information corresponding to wide-bandcolor information 20 may be 15 and narrow-band color informationcorresponding to wide-band color information 40 may be 25. For example,if the estimated wide-band color information W₃₀ is 40, the color imagecreator 140 may estimate narrow-band color information 461, 462, and 463to be 25.

In this example, the relative magnitudes of the narrow-band colorinformation and the estimated wide-band color information may be set tovarious values by a user. The color image creator 40 may estimate thenarrow-band color information based on the relative magnitudes of thenarrow-band color information and the estimated wide-band colorinformation. By the process described herein, the color image creator140 may estimate the remaining narrow-band color information.

The color image creator 140 may create a color image using the colorinformation illustrated in FIGS. 4G, 4H and 4I.

As described herein, the image processing method and apparatus mayestimate wide-band color information based on acquired narrow-band colorinformation. Also, the image processing method and apparatus may restorea full resolution or near full resolution of an image by restoring colorimage data using estimated wide-band color information.

The processes, functions, methods, and/or software described herein maybe recorded, stored, or fixed in one or more computer-readable storagemedia that includes program instructions to be implemented by a computerto cause a processor to execute or perform the program instructions. Themedia may also include, alone or in combination with the programinstructions, data files, data structures, and the like. The media andprogram instructions may be those specially designed and constructed, orthey may be of the kind well-known and available to those having skillin the computer software arts. Examples of computer-readable storagemedia include magnetic media, such as hard disks, floppy disks, andmagnetic tape; optical media such as CD ROM disks and DVDs;magneto-optical media, such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory (ROM), random access memory (RAM), flash memory, andthe like. Examples of program instructions include machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter. The described hardwaredevices may be configured to act as one or more software modules thatare recorded, stored, or fixed in one or more computer-readable storagemedia, in order to perform the operations and methods described above,or vice versa. In addition, a computer-readable storage medium may bedistributed among computer systems connected through a network andcomputer-readable codes or program instructions may be stored andexecuted in a decentralized manner.

As a non-exhaustive illustration only, the terminal device describedherein may refer to mobile devices such as a cellular phone, a personaldigital assistant (PDA), a digital camera, a portable game console, anMP3 player, a portable/personal multimedia player (PMP), a handhelde-book, a portable lab-top personal computer (PC), a global positioningsystem (GPS) navigation, and devices such as a desktop PC, a highdefinition television (HDTV), an optical disc player, a setup box, andthe like, capable of wireless communication or network communicationconsistent with that disclosed herein.

A computing system or a computer may include a microprocessor that iselectrically connected with a bus, a user interface, and a memorycontroller. It may further include a flash memory device. The flashmemory device may store N-bit data via the memory controller. The N-bitdata is processed or will be processed by the microprocessor and N maybe 1 or an integer greater than 1. Where the computing system orcomputer is a mobile apparatus, a battery may be additionally providedto supply operation voltage of the computing system or computer.

It should be apparent to those of ordinary skill in the art that thecomputing system or computer may further include an application chipset,a camera image processor (CIS), a mobile Dynamic Random Access Memory(DRAM), and the like. The memory controller and the flash memory devicemay constitute a solid state drive/disk (SSD) that uses a non-volatilememory to store data.

A number of examples have been described above. Nevertheless, it shouldbe understood that various modifications may be made. For example,suitable results may be achieved if the described techniques areperformed in a different order and/or if components in a describedsystem, architecture, device, or circuit are combined in a differentmanner and/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

1. An image processing apparatus comprising: a selector configured toselect at least one pixel from among pixels that correspond tonarrow-band color information of image data that includes narrow-bandcolor information and wide-band color information; and an estimatorconfigured to estimate wide-band color information of the selectedpixel, based on narrow-band color information of the selected pixel,narrow-band color information of pixels that are adjacent to theselected pixel, and correlation values between the narrow-band colorinformation and the wide-band color information.
 2. The image processingapparatus of claim 1, further comprising a region classifying unitconfigured to classify the image data into a homogeneity region and anon-homogeneity region using a Homogeneity theorem.
 3. The imageprocessing apparatus of claim 2, wherein the selector is configured toselect at least one pixel from among pixels that correspond to thenarrow-band color information in the non-homogeneity region.
 4. Theimage processing apparatus of claim 1, wherein the estimator is furtherconfigured to estimate wide-band color information of a pixel that isnot selected by the selector, based on the wide-band color informationof the image data and the wide-band color information estimated by theestimator.
 5. The image processing apparatus of claim 2, wherein theestimator is configured to estimate wide-band color information ofpixels that correspond to the narrow-band color information in thehomogeneity region, based on the wide-band color information of theimage data in the homogeneity region.
 6. The image processing apparatusof claim 1, wherein the narrow-band color information of pixels that areadjacent to the selected pixel is narrow-band color information of fourpixels, and the four pixels include the pixels that are directly above,below, to the left, and to the right of the at least one selected pixel.7. The image processing apparatus of claim 1, further comprising acalculator configured to divide the image data into 2×2 blocks that eachinclude three pixels corresponding to the narrow-band color informationand one pixel corresponding to the wide-band color information, and tocalculate correlation values between the wide-band color information andthe narrow-band color information included in each 2×2 block.
 8. Theimage processing apparatus of claim 1, wherein the selector isconfigured to select at least one pixel from among pixels thatcorrespond to the narrow-band color information, which are locateddiagonal to pixels corresponding to wide-band color information of theimage data.
 9. The image processing apparatus of claim 1, furthercomprising an image acquiring unit configured to acquire image data thatincludes narrow-band color information and wide-band color informationthat correspond to lights transmitted through a narrow-band color filterand a wide-band color filter, respectively, using photo sensors providedfor individual pixels.
 10. The image processing apparatus of claim 1,wherein the narrow-band color information is color information aboutlight transmitted through at least one of a Red (R) color filter, aGreen (G) color filter, a Blue (B) color filter, a Cyan (C) colorfilter, a Yellow (Y) color filter, a Magenta (M) color filter and aBlack (K) color filter, and the wide-band color information is colorinformation about light transmitted through at least one of apanchromatic filter and a White & Near Infrared (WNIR) filter.
 11. Animage processing method comprising: selecting at least one pixel fromamong pixels that correspond to narrow-band color information of imagedata that includes the narrow-band color information and wide-band colorinformation; and estimating wide-band color information of the selectedpixel based on narrow-band color information of the selected pixel,narrow-band color information of pixels that are adjacent to theselected pixels, and correlation values between the narrow-band colorinformation and the wide-band color information.
 12. The imageprocessing method of claim 11, further comprising classifying the imagedata into a homogeneity region and a non-homogeneity region using aHomogeneity theorem.
 13. The image processing method of claim 12,wherein the selecting of the at least one pixel comprises selecting atleast one pixel from among pixels that correspond to the narrow-bandcolor information in the non-homogeneity region.
 14. The imageprocessing method of claim 11, wherein the estimating of the wide-bandcolor information further comprises estimating wide-band colorinformation of a pixel that is not selected, based on the wide-bandcolor information of the image data and the estimated wide-band colorinformation.
 15. The image processing method of claim 12, wherein theestimating of the wide-band color information comprises estimatingwide-band color information of pixels that correspond to narrow-bandcolor information in the homogeneity region, based on the wide-bandcolor information of the image data in the homogeneity region.
 16. Theimage processing method of claim 11, wherein the narrow-band colorinformation of the pixels adjacent to the selected pixel is narrow-bandcolor information of four pixels, and the four pixels include the pixelsthat are directly above, below, to the left, and to the right of the atleast one selected pixel.
 17. The image processing method of claim 11,further comprising dividing the image data into 2×2 blocks that eachinclude three pixels corresponding to the narrow-band color informationand one pixel corresponding to the wide-band color information, andcalculating correlation values between the wide-band color informationand the narrow-band color information included in each 2×2 block. 18.The image processing method of claim 11, wherein the selecting of the atleast one pixel comprises selecting at least one pixel from among pixelsthat correspond to the narrow-band color information, which are locateddiagonal to pixels corresponding to the wide-band color information ofthe image data.
 19. The image processing method of claim 11, furthercomprising acquiring image data that includes narrow-band colorinformation and wide-band color information that corresponds to lightsthat are transmitted through a narrow-band color filter and a wide-bandcolor filter, using photo sensors provided for individual pixels. 20.The image processing method of claim 11, wherein the narrow-band colorinformation is color information about light transmitted through atleast one of a Red (R) color filter, a Green (G) color filter, a Blue(B) color filter, a Cyan (C) color filter, a Yellow (Y) color filter, aMagenta (M) color filter and a Black (K) color filter, and the wide-bandcolor information is color information about light transmitted throughat least one of a panchromatic filter and a White & Near Infrared (WNIR)filter.